Assignment 03

Author

Affiliation

Dakota Alder - dkalder

Boston University

1 Loading the Dataset

import pandas as pd
import plotly.express as px
import plotly.io as pio
from pyspark.sql import SparkSession
import re
import numpy as np
import plotly.graph_objects as go
from pyspark.sql.functions import col, split, explode, regexp_replace, transform, when
from pyspark.sql import functions as F
from pyspark.sql.functions import col, monotonically_increasing_id

np.random.seed(42)

pio.renderers.default = "notebook"

# Initialize Spark Session
spark = SparkSession.builder.appName("LightcastData").getOrCreate()

# Load Data
df = spark.read.option("header", "true").option("inferSchema", "true").option("multiLine","true").option("escape", "\"").csv("data/lightcast_job_postings.csv")
df.createOrReplaceTempView("job_postings")

# Show Schema and Sample Data
#print("---This is Diagnostic check, No need to print it in the final doc---")

#df.printSchema() # comment this line when rendering the submission
#df.show(5)

WARNING: Using incubator modules: jdk.incubator.vector
Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
25/09/24 22:59:57 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[Stage 0:>                                                          (0 + 1) / 1]                                                                                [Stage 1:>                                                          (0 + 1) / 1]                                                                                25/09/24 23:00:12 WARN SparkStringUtils: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.

2 Data Preparation

[Stage 2:>                                                          (0 + 1) / 1]                                                                                [Stage 3:>                                                          (0 + 1) / 1]                                                                                [Stage 4:>                                                          (0 + 1) / 1]                                                                                

Medians: 87295.0 130042.0 115024.0

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Data Cleaning Complete. Rows retained: 72498

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